Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations607
Missing cells0
Missing cells (%)0.0%
Duplicate rows29
Duplicate rows (%)4.8%
Total size in memory296.4 KiB
Average record size in memory500.1 B

Variable types

Categorical11
Numeric14
Boolean12

Alerts

Dataset has 29 (4.8%) duplicate rowsDuplicates
Cost of Living Index is highly overall correlated with Cost of Living Plus Rent Index and 15 other fieldsHigh correlation
Cost of Living Plus Rent Index is highly overall correlated with Cost of Living Index and 16 other fieldsHigh correlation
Country is highly overall correlated with Cost of Living Index and 17 other fieldsHigh correlation
GDP is highly overall correlated with Cost of Living Index and 16 other fieldsHigh correlation
Local Purchasing Power Index is highly overall correlated with Cost of Living Index and 16 other fieldsHigh correlation
Rent Index is highly overall correlated with Cost of Living Index and 17 other fieldsHigh correlation
adjusted_salary is highly overall correlated with log_salary and 2 other fieldsHigh correlation
company_size is highly overall correlated with company_size_num and 1 other fieldsHigh correlation
company_size_num is highly overall correlated with company_size and 1 other fieldsHigh correlation
country_France is highly overall correlated with Country and 2 other fieldsHigh correlation
country_Germany is highly overall correlated with Country and 2 other fieldsHigh correlation
country_Greece is highly overall correlated with Cost of Living Index and 5 other fieldsHigh correlation
country_India is highly overall correlated with Cost of Living Index and 9 other fieldsHigh correlation
country_Japan is highly overall correlated with CountryHigh correlation
country_Netherlands is highly overall correlated with CountryHigh correlation
country_Other is highly overall correlated with Cost of Living Index and 7 other fieldsHigh correlation
country_Spain is highly overall correlated with Cost of Living Index and 5 other fieldsHigh correlation
country_United Kingdom is highly overall correlated with Cost of Living Plus Rent Index and 6 other fieldsHigh correlation
country_United States is highly overall correlated with Cost of Living Index and 11 other fieldsHigh correlation
exp_x_remote is highly overall correlated with experience_level and 4 other fieldsHigh correlation
exp_x_role is highly overall correlated with experience_level and 4 other fieldsHigh correlation
experience_level is highly overall correlated with exp_x_remote and 2 other fieldsHigh correlation
experience_numeric is highly overall correlated with exp_x_remote and 2 other fieldsHigh correlation
gdp_to_power_ratio is highly overall correlated with Cost of Living Index and 15 other fieldsHigh correlation
job_title is highly overall correlated with exp_x_role and 2 other fieldsHigh correlation
log_salary is highly overall correlated with Cost of Living Index and 12 other fieldsHigh correlation
remote_Onsite is highly overall correlated with exp_x_remote and 2 other fieldsHigh correlation
remote_Remote is highly overall correlated with exp_x_remote and 2 other fieldsHigh correlation
remote_ratio is highly overall correlated with exp_x_remote and 2 other fieldsHigh correlation
rent_burden_ratio is highly overall correlated with Cost of Living Index and 17 other fieldsHigh correlation
role_category is highly overall correlated with exp_x_role and 2 other fieldsHigh correlation
role_numeric is highly overall correlated with exp_x_role and 2 other fieldsHigh correlation
salary_in_usd is highly overall correlated with Cost of Living Index and 11 other fieldsHigh correlation
salary_k is highly overall correlated with Cost of Living Index and 11 other fieldsHigh correlation
size_x_gdp is highly overall correlated with Cost of Living Index and 14 other fieldsHigh correlation
work_year is highly overall correlated with gdp_to_power_ratioHigh correlation
Country is highly imbalanced (50.4%)Imbalance
employment_type is highly imbalanced (87.7%)Imbalance
country_France is highly imbalanced (83.3%)Imbalance
country_Germany is highly imbalanced (73.0%)Imbalance
country_Greece is highly imbalanced (86.9%)Imbalance
country_India is highly imbalanced (76.0%)Imbalance
country_Japan is highly imbalanced (92.0%)Imbalance
country_Netherlands is highly imbalanced (94.3%)Imbalance
country_Spain is highly imbalanced (84.2%)Imbalance
country_United Kingdom is highly imbalanced (60.7%)Imbalance
exp_x_role has 53 (8.7%) zerosZeros
exp_x_remote has 127 (20.9%) zerosZeros

Reproduction

Analysis started2025-11-08 10:51:05.281606
Analysis finished2025-11-08 10:51:25.285298
Duration20 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Country
Categorical

High correlation  Imbalance 

Distinct50
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size35.8 KiB
United States
355 
United Kingdom
47 
Canada
 
30
Germany
 
28
India
 
24
Other values (45)
123 

Length

Max length25
Median length13
Mean length11.026359
Min length4

Characters and Unicode

Total characters6,693
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)3.5%

Sample

1st rowGermany
2nd rowJapan
3rd rowUnited Kingdom
4th rowHonduras
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States355
58.5%
United Kingdom47
 
7.7%
Canada30
 
4.9%
Germany28
 
4.6%
India24
 
4.0%
France15
 
2.5%
Spain14
 
2.3%
Greece11
 
1.8%
Japan6
 
1.0%
Netherlands4
 
0.7%
Other values (40)73
 
12.0%

Length

2025-11-08T15:51:25.340344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united405
39.5%
states355
34.6%
kingdom47
 
4.6%
canada30
 
2.9%
germany28
 
2.7%
india24
 
2.3%
france15
 
1.5%
spain14
 
1.4%
greece11
 
1.1%
japan6
 
0.6%
Other values (49)90
 
8.8%

Most occurring characters

ValueCountFrequency (%)
t1146
17.1%
e886
13.2%
a623
9.3%
n604
9.0%
i544
8.1%
d522
7.8%
418
 
6.2%
U406
 
6.1%
s381
 
5.7%
S375
 
5.6%
Other values (40)788
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)6693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t1146
17.1%
e886
13.2%
a623
9.3%
n604
9.0%
i544
8.1%
d522
7.8%
418
 
6.2%
U406
 
6.1%
s381
 
5.7%
S375
 
5.6%
Other values (40)788
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t1146
17.1%
e886
13.2%
a623
9.3%
n604
9.0%
i544
8.1%
d522
7.8%
418
 
6.2%
U406
 
6.1%
s381
 
5.7%
S375
 
5.6%
Other values (40)788
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t1146
17.1%
e886
13.2%
a623
9.3%
n604
9.0%
i544
8.1%
d522
7.8%
418
 
6.2%
U406
 
6.1%
s381
 
5.7%
S375
 
5.6%
Other values (40)788
11.8%

work_year
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size31.5 KiB
2022
318 
2021
217 
2020
72 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2,428
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2022318
52.4%
2021217
35.7%
202072
 
11.9%

Length

2025-11-08T15:51:25.423497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:51:25.472824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2022318
52.4%
2021217
35.7%
202072
 
11.9%

Most occurring characters

ValueCountFrequency (%)
21532
63.1%
0679
28.0%
1217
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21532
63.1%
0679
28.0%
1217
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21532
63.1%
0679
28.0%
1217
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21532
63.1%
0679
28.0%
1217
 
8.9%

experience_level
Categorical

High correlation 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size32.1 KiB
Senior
280 
Mid
213 
Entry
88 
Executive
 
26

Length

Max length9
Median length6
Mean length4.9308072
Min length3

Characters and Unicode

Total characters2,993
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMid
2nd rowSenior
3rd rowSenior
4th rowMid
5th rowSenior

Common Values

ValueCountFrequency (%)
Senior280
46.1%
Mid213
35.1%
Entry88
 
14.5%
Executive26
 
4.3%

Length

2025-11-08T15:51:25.546275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:51:25.600348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
senior280
46.1%
mid213
35.1%
entry88
 
14.5%
executive26
 
4.3%

Most occurring characters

ValueCountFrequency (%)
i519
17.3%
r368
12.3%
n368
12.3%
e332
11.1%
S280
9.4%
o280
9.4%
M213
7.1%
d213
7.1%
E114
 
3.8%
t114
 
3.8%
Other values (5)192
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2993
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i519
17.3%
r368
12.3%
n368
12.3%
e332
11.1%
S280
9.4%
o280
9.4%
M213
7.1%
d213
7.1%
E114
 
3.8%
t114
 
3.8%
Other values (5)192
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2993
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i519
17.3%
r368
12.3%
n368
12.3%
e332
11.1%
S280
9.4%
o280
9.4%
M213
7.1%
d213
7.1%
E114
 
3.8%
t114
 
3.8%
Other values (5)192
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2993
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i519
17.3%
r368
12.3%
n368
12.3%
e332
11.1%
S280
9.4%
o280
9.4%
M213
7.1%
d213
7.1%
E114
 
3.8%
t114
 
3.8%
Other values (5)192
 
6.4%

employment_type
Categorical

Imbalance 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size30.4 KiB
FT
588 
PT
 
10
CT
 
5
FL
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1,214
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFT
2nd rowFT
3rd rowFT
4th rowFT
5th rowFT

Common Values

ValueCountFrequency (%)
FT588
96.9%
PT10
 
1.6%
CT5
 
0.8%
FL4
 
0.7%

Length

2025-11-08T15:51:25.673617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:51:25.719650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ft588
96.9%
pt10
 
1.6%
ct5
 
0.8%
fl4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
T603
49.7%
F592
48.8%
P10
 
0.8%
C5
 
0.4%
L4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T603
49.7%
F592
48.8%
P10
 
0.8%
C5
 
0.4%
L4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T603
49.7%
F592
48.8%
P10
 
0.8%
C5
 
0.4%
L4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T603
49.7%
F592
48.8%
P10
 
0.8%
C5
 
0.4%
L4
 
0.3%

job_title
Categorical

High correlation 

Distinct50
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size38.8 KiB
Data Scientist
143 
Data Engineer
132 
Data Analyst
97 
Machine Learning Engineer
41 
Research Scientist
 
16
Other values (45)
178 

Length

Max length40
Median length33
Mean length16.258649
Min length11

Characters and Unicode

Total characters9,869
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.8%

Sample

1st rowData Scientist
2nd rowMachine Learning Scientist
3rd rowBig Data Engineer
4th rowProduct Data Analyst
5th rowMachine Learning Engineer

Common Values

ValueCountFrequency (%)
Data Scientist143
23.6%
Data Engineer132
21.7%
Data Analyst97
16.0%
Machine Learning Engineer41
 
6.8%
Research Scientist16
 
2.6%
Data Science Manager12
 
2.0%
Data Architect11
 
1.8%
Machine Learning Scientist8
 
1.3%
Big Data Engineer8
 
1.3%
Director of Data Science7
 
1.2%
Other values (40)132
21.7%

Length

2025-11-08T15:51:25.788658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
data499
35.0%
engineer223
15.6%
scientist194
 
13.6%
analyst119
 
8.3%
machine62
 
4.3%
learning62
 
4.3%
science33
 
2.3%
manager25
 
1.8%
of19
 
1.3%
research16
 
1.1%
Other values (30)175
 
12.3%

Most occurring characters

ValueCountFrequency (%)
a1381
14.0%
t1093
11.1%
n1091
11.1%
e997
10.1%
i894
9.1%
820
8.3%
D514
 
5.2%
c409
 
4.1%
r407
 
4.1%
s382
 
3.9%
Other values (29)1881
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)9869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1381
14.0%
t1093
11.1%
n1091
11.1%
e997
10.1%
i894
9.1%
820
8.3%
D514
 
5.2%
c409
 
4.1%
r407
 
4.1%
s382
 
3.9%
Other values (29)1881
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1381
14.0%
t1093
11.1%
n1091
11.1%
e997
10.1%
i894
9.1%
820
8.3%
D514
 
5.2%
c409
 
4.1%
r407
 
4.1%
s382
 
3.9%
Other values (29)1881
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1381
14.0%
t1093
11.1%
n1091
11.1%
e997
10.1%
i894
9.1%
820
8.3%
D514
 
5.2%
c409
 
4.1%
r407
 
4.1%
s382
 
3.9%
Other values (29)1881
19.1%

salary_in_usd
Real number (ℝ)

High correlation 

Distinct369
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112297.87
Minimum2859
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:25.880814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2859
5-th percentile20000
Q162726
median101570
Q3150000
95-th percentile220110
Maximum600000
Range597141
Interquartile range (IQR)87274

Descriptive statistics

Standard deviation70957.259
Coefficient of variation (CV)0.63186648
Kurtosis6.3537947
Mean112297.87
Median Absolute Deviation (MAD)42430
Skewness1.667545
Sum68164807
Variance5.0349327 × 109
MonotonicityNot monotonic
2025-11-08T15:51:25.975038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000015
 
2.5%
12000012
 
2.0%
15000012
 
2.0%
20000010
 
1.6%
1350009
 
1.5%
1700008
 
1.3%
1400008
 
1.3%
800008
 
1.3%
1300008
 
1.3%
1600008
 
1.3%
Other values (359)509
83.9%
ValueCountFrequency (%)
28591
0.2%
40002
0.3%
54091
0.2%
56791
0.2%
57071
0.2%
58821
0.2%
60722
0.3%
80001
0.2%
92721
0.2%
94661
0.2%
ValueCountFrequency (%)
6000001
0.2%
4500002
0.3%
4230001
0.2%
4160001
0.2%
4120001
0.2%
4050001
0.2%
3800001
0.2%
3250001
0.2%
3240001
0.2%
2760001
0.2%

remote_ratio
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size30.6 KiB
100
381 
0
127 
50
99 

Length

Max length3
Median length3
Mean length2.4184514
Min length1

Characters and Unicode

Total characters1,468
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row50
4th row0
5th row50

Common Values

ValueCountFrequency (%)
100381
62.8%
0127
 
20.9%
5099
 
16.3%

Length

2025-11-08T15:51:26.059961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:51:26.109496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
100381
62.8%
0127
 
20.9%
5099
 
16.3%

Most occurring characters

ValueCountFrequency (%)
0988
67.3%
1381
 
26.0%
599
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0988
67.3%
1381
 
26.0%
599
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0988
67.3%
1381
 
26.0%
599
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0988
67.3%
1381
 
26.0%
599
 
6.7%

company_size
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
M
326 
L
198 
S
83 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters607
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL
2nd rowS
3rd rowM
4th rowS
5th rowL

Common Values

ValueCountFrequency (%)
M326
53.7%
L198
32.6%
S83
 
13.7%

Length

2025-11-08T15:51:26.168424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:51:26.218267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m326
53.7%
l198
32.6%
s83
 
13.7%

Most occurring characters

ValueCountFrequency (%)
M326
53.7%
L198
32.6%
S83
 
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M326
53.7%
L198
32.6%
S83
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M326
53.7%
L198
32.6%
S83
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M326
53.7%
L198
32.6%
S83
 
13.7%

Cost of Living Index
Real number (ℝ)

High correlation 

Distinct41
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.606218
Minimum18.8
Maximum101.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:26.281376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile29.51
Q162.2
median70.4
Q370.4
95-th percentile70.4
Maximum101.1
Range82.3
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation12.782832
Coefficient of variation (CV)0.20096828
Kurtosis4.2730822
Mean63.606218
Median Absolute Deviation (MAD)0
Skewness-2.1051366
Sum38608.975
Variance163.4008
MonotonicityNot monotonic
2025-11-08T15:51:26.376348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
70.4355
58.5%
6247
 
7.7%
64.830
 
4.9%
62.228
 
4.6%
21.224
 
4.0%
63.715
 
2.5%
47.314
 
2.3%
63.6062184912
 
2.0%
5212
 
2.0%
46.16
 
1.0%
Other values (31)64
 
10.5%
ValueCountFrequency (%)
18.83
 
0.5%
21.224
4.0%
25.91
 
0.2%
28.81
 
0.2%
28.91
 
0.2%
29.31
 
0.2%
301
 
0.2%
30.24
 
0.7%
31.42
 
0.3%
31.72
 
0.3%
ValueCountFrequency (%)
101.12
 
0.3%
76.71
 
0.2%
72.33
 
0.5%
70.4355
58.5%
70.23
 
0.5%
65.14
 
0.7%
64.830
 
4.9%
64.61
 
0.2%
64.41
 
0.2%
63.715
 
2.5%

Rent Index
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.282185
Minimum2.8
Maximum67.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:26.448540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile7.76
Q131.1
median41.7
Q341.7
95-th percentile41.7
Maximum67.2
Range64.4
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation11.08575
Coefficient of variation (CV)0.32336765
Kurtosis0.68638655
Mean34.282185
Median Absolute Deviation (MAD)0
Skewness-1.2742972
Sum20809.286
Variance122.89385
MonotonicityNot monotonic
2025-11-08T15:51:26.530392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
41.7355
58.5%
31.147
 
7.7%
33.230
 
4.9%
24.428
 
4.6%
5.624
 
4.0%
2115
 
2.5%
22.914
 
2.3%
34.2821848712
 
2.0%
12.911
 
1.8%
13.46
 
1.0%
Other values (32)65
 
10.7%
ValueCountFrequency (%)
2.83
 
0.5%
3.81
 
0.2%
5.624
4.0%
7.62
 
0.3%
7.71
 
0.2%
7.93
 
0.5%
8.51
 
0.2%
8.61
 
0.2%
9.91
 
0.2%
11.11
 
0.2%
ValueCountFrequency (%)
67.21
 
0.2%
46.52
 
0.3%
42.31
 
0.2%
41.7355
58.5%
41.33
 
0.5%
40.53
 
0.5%
34.2821848712
 
2.0%
33.54
 
0.7%
33.43
 
0.5%
33.230
 
4.9%

Local Purchasing Power Index
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.58891
Minimum11
Maximum182.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:26.650399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile56.9
Q1115.2
median142.3
Q3142.3
95-th percentile142.3
Maximum182.5
Range171.5
Interquartile range (IQR)27.1

Descriptive statistics

Standard deviation27.571411
Coefficient of variation (CV)0.22129908
Kurtosis2.3216158
Mean124.58891
Median Absolute Deviation (MAD)0
Skewness-1.5925849
Sum75625.467
Variance760.18272
MonotonicityNot monotonic
2025-11-08T15:51:26.729407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
142.3355
58.5%
115.247
 
7.7%
103.730
 
4.9%
120.228
 
4.6%
82.624
 
4.0%
102.419
 
3.1%
92.114
 
2.3%
124.588907612
 
2.0%
5311
 
1.8%
1176
 
1.0%
Other values (32)61
 
10.0%
ValueCountFrequency (%)
112
0.3%
29.13
0.5%
29.91
 
0.2%
33.71
 
0.2%
34.21
 
0.2%
37.23
0.5%
39.51
 
0.2%
45.11
 
0.2%
45.43
0.5%
50.41
 
0.2%
ValueCountFrequency (%)
182.53
 
0.5%
158.72
 
0.3%
142.3355
58.5%
127.93
 
0.5%
127.43
 
0.5%
127.23
 
0.5%
124.94
 
0.7%
124.588907612
 
2.0%
1211
 
0.2%
120.228
 
4.6%

Cost of Living Plus Rent Index
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.507227
Minimum11.1
Maximum74.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:26.808598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.1
5-th percentile19.16
Q147.1
median56.6
Q356.6
95-th percentile56.6
Maximum74.9
Range63.8
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation11.670647
Coefficient of variation (CV)0.23573622
Kurtosis2.5962063
Mean49.507227
Median Absolute Deviation (MAD)0
Skewness-1.7661104
Sum30050.887
Variance136.20399
MonotonicityNot monotonic
2025-11-08T15:51:26.892599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
56.6355
58.5%
47.147
 
7.7%
49.630
 
4.9%
4428
 
4.6%
13.724
 
4.0%
43.215
 
2.5%
35.614
 
2.3%
49.5072268912
 
2.0%
33.211
 
1.8%
48.97
 
1.2%
Other values (32)64
 
10.5%
ValueCountFrequency (%)
11.13
 
0.5%
13.724
4.0%
16.91
 
0.2%
17.11
 
0.2%
18.91
 
0.2%
19.11
 
0.2%
19.31
 
0.2%
19.53
 
0.5%
19.81
 
0.2%
22.42
 
0.3%
ValueCountFrequency (%)
74.92
 
0.3%
72.11
 
0.2%
56.6355
58.5%
53.81
 
0.2%
52.53
 
0.5%
51.93
 
0.5%
50.23
 
0.5%
49.630
 
4.9%
49.5072268912
 
2.0%
48.97
 
1.2%

GDP
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59686.296
Minimum1278.3974
Maximum134965.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:26.977562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1278.3974
5-th percentile2754.4719
Q146926.476
median71307.402
Q377860.911
95-th percentile77860.911
Maximum134965.82
Range133687.42
Interquartile range (IQR)30934.436

Descriptive statistics

Standard deviation23330.865
Coefficient of variation (CV)0.39089148
Kurtosis0.52156789
Mean59686.296
Median Absolute Deviation (MAD)6553.5096
Skewness-1.0306373
Sum36229582
Variance5.4432924 × 108
MonotonicityNot monotonic
2025-11-08T15:51:27.066210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77860.91129226
37.2%
71307.4017399
16.3%
64401.5074430
 
4.9%
46062.9914129
 
4.8%
56256.8007318
 
3.0%
2239.61384416
 
2.6%
52265.6541616
 
2.6%
46926.4757414
 
2.3%
52886.6616411
 
1.8%
20971.834939
 
1.5%
Other values (75)139
22.9%
ValueCountFrequency (%)
1278.3973521
 
0.2%
1455.3192241
 
0.2%
1538.3228131
 
0.2%
1907.0425164
 
0.7%
2017.2748651
 
0.2%
2019.6570631
 
0.2%
2061.3562211
 
0.2%
2239.61384416
2.6%
2307.6149431
 
0.2%
2347.4482944
 
0.7%
ValueCountFrequency (%)
134965.81541
 
0.2%
123719.65891
 
0.2%
116860.02821
 
0.2%
105234.51161
 
0.2%
94394.510681
 
0.2%
93664.773671
 
0.2%
80056.128821
 
0.2%
77860.91129226
37.2%
71307.4017399
16.3%
69727.987372
 
0.3%

log_salary
Real number (ℝ)

High correlation 

Distinct369
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.397414
Minimum7.9585769
Maximum13.304687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:27.157156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.9585769
5-th percentile9.9035376
Q111.046547
median11.528513
Q311.918397
95-th percentile12.301887
Maximum13.304687
Range5.3461097
Interquartile range (IQR)0.87184998

Descriptive statistics

Standard deviation0.77412266
Coefficient of variation (CV)0.067920903
Kurtosis2.4767323
Mean11.397414
Median Absolute Deviation (MAD)0.42267351
Skewness-1.2609005
Sum6918.2304
Variance0.59926589
MonotonicityNot monotonic
2025-11-08T15:51:27.246724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.5129354615
 
2.5%
11.6952553612
 
2.0%
11.9183972412
 
2.0%
12.2060776510
 
1.6%
11.813037469
 
1.5%
12.04355968
 
1.3%
11.849404848
 
1.3%
11.289794418
 
1.3%
11.775297428
 
1.3%
11.982935348
 
1.3%
Other values (359)509
83.9%
ValueCountFrequency (%)
7.9585769041
0.2%
8.2942996092
0.3%
8.5960043721
0.2%
8.6447065121
0.2%
8.6496239791
0.2%
8.6798221151
0.2%
8.7116079962
0.3%
8.9873218131
0.2%
9.1348622311
0.2%
9.1555673461
0.2%
ValueCountFrequency (%)
13.30468661
0.2%
13.017005082
0.3%
12.955129821
0.2%
12.938442941
0.2%
12.928781061
0.2%
12.911644821
0.2%
12.847929161
0.2%
12.691583541
0.2%
12.688501881
0.2%
12.528159771
0.2%

experience_numeric
Categorical

High correlation 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
3
280 
2
213 
1
88 
4
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters607
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3280
46.1%
2213
35.1%
188
 
14.5%
426
 
4.3%

Length

2025-11-08T15:51:27.326451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:51:27.375476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3280
46.1%
2213
35.1%
188
 
14.5%
426
 
4.3%

Most occurring characters

ValueCountFrequency (%)
3280
46.1%
2213
35.1%
188
 
14.5%
426
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3280
46.1%
2213
35.1%
188
 
14.5%
426
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3280
46.1%
2213
35.1%
188
 
14.5%
426
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3280
46.1%
2213
35.1%
188
 
14.5%
426
 
4.3%

salary_k
Real number (ℝ)

High correlation 

Distinct346
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.29736
Minimum2.9
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:27.442791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile20
Q162.7
median101.6
Q3150
95-th percentile220.1
Maximum600
Range597.1
Interquartile range (IQR)87.3

Descriptive statistics

Standard deviation70.957005
Coefficient of variation (CV)0.63186706
Kurtosis6.3539013
Mean112.29736
Median Absolute Deviation (MAD)42.4
Skewness1.6676019
Sum68164.5
Variance5034.8966
MonotonicityNot monotonic
2025-11-08T15:51:27.528933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10015
 
2.5%
12012
 
2.0%
15012
 
2.0%
20010
 
1.6%
1359
 
1.5%
1309
 
1.5%
1708
 
1.3%
808
 
1.3%
1608
 
1.3%
1408
 
1.3%
Other values (336)508
83.7%
ValueCountFrequency (%)
2.91
0.2%
42
0.3%
5.41
0.2%
5.72
0.3%
5.91
0.2%
6.12
0.3%
81
0.2%
9.31
0.2%
9.51
0.2%
102
0.3%
ValueCountFrequency (%)
6001
0.2%
4502
0.3%
4231
0.2%
4161
0.2%
4121
0.2%
4051
0.2%
3801
0.2%
3251
0.2%
3241
0.2%
2761
0.2%

adjusted_salary
Real number (ℝ)

High correlation 

Distinct412
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1720.2873
Minimum58.18
Maximum8522.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:27.620648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum58.18
5-th percentile431.171
Q11060.35
median1562.5
Q32158.365
95-th percentile3338.07
Maximum8522.73
Range8464.55
Interquartile range (IQR)1098.015

Descriptive statistics

Standard deviation988.69483
Coefficient of variation (CV)0.57472656
Kurtosis6.6631624
Mean1720.2873
Median Absolute Deviation (MAD)557.28
Skewness1.7780899
Sum1044214.4
Variance977517.46
MonotonicityNot monotonic
2025-11-08T15:51:27.706104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1420.4512
 
2.0%
1704.5511
 
1.8%
2130.6811
 
1.8%
2840.9110
 
1.6%
1917.619
 
1.5%
2414.778
 
1.3%
1988.648
 
1.3%
2272.738
 
1.3%
1136.368
 
1.3%
1278.416
 
1.0%
Other values (402)516
85.0%
ValueCountFrequency (%)
58.181
0.2%
62.892
0.3%
71.121
0.2%
80.671
0.2%
160.261
0.2%
170.452
0.3%
190.281
0.2%
218.91
0.2%
255.141
0.2%
256.691
0.2%
ValueCountFrequency (%)
8522.731
0.2%
6392.052
0.3%
6008.521
0.2%
5909.091
0.2%
5852.271
0.2%
5752.841
0.2%
5639.911
0.2%
5397.731
0.2%
4616.481
0.2%
4602.271
0.2%

rent_burden_ratio
Real number (ℝ)

High correlation 

Distinct41
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66952718
Minimum0.225
Maximum0.932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:27.782479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.225
5-th percentile0.409
Q10.66
median0.737
Q30.737
95-th percentile0.737
Maximum0.932
Range0.707
Interquartile range (IQR)0.077

Descriptive statistics

Standard deviation0.11171403
Coefficient of variation (CV)0.16685511
Kurtosis1.5265627
Mean0.66952718
Median Absolute Deviation (MAD)0
Skewness-1.5349706
Sum406.403
Variance0.012480025
MonotonicityNot monotonic
2025-11-08T15:51:27.863023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.737355
58.5%
0.6647
 
7.7%
0.66930
 
4.9%
0.55528
 
4.6%
0.40925
 
4.1%
0.48617
 
2.8%
0.64314
 
2.3%
0.69212
 
2.0%
0.38911
 
1.8%
0.4416
 
1.0%
Other values (31)62
 
10.2%
ValueCountFrequency (%)
0.2251
 
0.2%
0.2523
 
0.5%
0.38911
1.8%
0.3991
 
0.2%
0.4021
 
0.2%
0.4053
 
0.5%
0.40925
4.1%
0.4171
 
0.2%
0.4291
 
0.2%
0.4321
 
0.2%
ValueCountFrequency (%)
0.9321
 
0.2%
0.8453
 
0.5%
0.8192
 
0.3%
0.7861
 
0.2%
0.783
 
0.5%
0.737355
58.5%
0.69212
 
2.0%
0.6864
 
0.7%
0.6854
 
0.7%
0.66930
 
4.9%

gdp_to_power_ratio
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.86053
Minimum18.522
Maximum1040.895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:27.955826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.522
5-th percentile97.5363
Q1407.348
median501.106
Q3547.16
95-th percentile547.16
Maximum1040.895
Range1022.373
Interquartile range (IQR)139.812

Descriptive statistics

Standard deviation135.25882
Coefficient of variation (CV)0.2954149
Kurtosis3.6453773
Mean457.86053
Median Absolute Deviation (MAD)46.054
Skewness-1.7192617
Sum277921.34
Variance18294.949
MonotonicityNot monotonic
2025-11-08T15:51:28.057108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
547.16226
37.2%
501.10699
16.3%
452.57630
 
4.9%
399.85229
 
4.8%
542.49618
 
3.0%
27.11416
 
2.6%
434.82216
 
2.6%
407.34814
 
2.3%
509.99711
 
1.8%
395.6959
 
1.5%
Other values (75)139
22.9%
ValueCountFrequency (%)
18.5221
 
0.2%
23.0884
 
0.7%
27.11416
2.6%
28.4194
 
0.7%
29.7311
 
0.2%
43.9311
 
0.2%
50.0111
 
0.2%
52.8631
 
0.2%
60.2741
 
0.2%
96.5971
 
0.2%
ValueCountFrequency (%)
1040.8951
 
0.2%
739.5391
 
0.2%
720.5771
 
0.2%
677.9161
 
0.2%
640.3291
 
0.2%
594.7981
 
0.2%
590.21
 
0.2%
558.9631
 
0.2%
548.1762
 
0.3%
547.16226
37.2%

role_category
Categorical

High correlation 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size33.0 KiB
AI
235 
Engineering
172 
Analytics
135 
Other
53 
Management
 
12

Length

Max length11
Median length10
Mean length6.5271829
Min length2

Characters and Unicode

Total characters3,962
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAI
2nd rowAI
3rd rowEngineering
4th rowAnalytics
5th rowAI

Common Values

ValueCountFrequency (%)
AI235
38.7%
Engineering172
28.3%
Analytics135
22.2%
Other53
 
8.7%
Management12
 
2.0%

Length

2025-11-08T15:51:28.129092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:51:28.178437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ai235
38.7%
engineering172
28.3%
analytics135
22.2%
other53
 
8.7%
management12
 
2.0%

Most occurring characters

ValueCountFrequency (%)
n675
17.0%
i479
12.1%
e421
10.6%
A370
9.3%
g356
9.0%
I235
 
5.9%
r225
 
5.7%
t200
 
5.0%
E172
 
4.3%
a159
 
4.0%
Other values (8)670
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)3962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n675
17.0%
i479
12.1%
e421
10.6%
A370
9.3%
g356
9.0%
I235
 
5.9%
r225
 
5.7%
t200
 
5.0%
E172
 
4.3%
a159
 
4.0%
Other values (8)670
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n675
17.0%
i479
12.1%
e421
10.6%
A370
9.3%
g356
9.0%
I235
 
5.9%
r225
 
5.7%
t200
 
5.0%
E172
 
4.3%
a159
 
4.0%
Other values (8)670
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n675
17.0%
i479
12.1%
e421
10.6%
A370
9.3%
g356
9.0%
I235
 
5.9%
r225
 
5.7%
t200
 
5.0%
E172
 
4.3%
a159
 
4.0%
Other values (8)670
16.9%

role_numeric
Categorical

High correlation 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
4
235 
2
172 
3
135 
0
53 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters607
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4235
38.7%
2172
28.3%
3135
22.2%
053
 
8.7%
112
 
2.0%

Length

2025-11-08T15:51:28.255658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:51:28.310769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4235
38.7%
2172
28.3%
3135
22.2%
053
 
8.7%
112
 
2.0%

Most occurring characters

ValueCountFrequency (%)
4235
38.7%
2172
28.3%
3135
22.2%
053
 
8.7%
112
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4235
38.7%
2172
28.3%
3135
22.2%
053
 
8.7%
112
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4235
38.7%
2172
28.3%
3135
22.2%
053
 
8.7%
112
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4235
38.7%
2172
28.3%
3135
22.2%
053
 
8.7%
112
 
2.0%

remote_Onsite
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
480 
True
127 
ValueCountFrequency (%)
False480
79.1%
True127
 
20.9%
2025-11-08T15:51:28.359509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

remote_Remote
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
True
381 
False
226 
ValueCountFrequency (%)
True381
62.8%
False226
37.2%
2025-11-08T15:51:28.394563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

company_size_num
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size29.8 KiB
2
326 
3
198 
1
83 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters607
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row2
4th row1
5th row3

Common Values

ValueCountFrequency (%)
2326
53.7%
3198
32.6%
183
 
13.7%

Length

2025-11-08T15:51:28.444441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T15:51:28.491485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2326
53.7%
3198
32.6%
183
 
13.7%

Most occurring characters

ValueCountFrequency (%)
2326
53.7%
3198
32.6%
183
 
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2326
53.7%
3198
32.6%
183
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2326
53.7%
3198
32.6%
183
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2326
53.7%
3198
32.6%
183
 
13.7%

country_France
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
592 
True
 
15
ValueCountFrequency (%)
False592
97.5%
True15
 
2.5%
2025-11-08T15:51:28.525491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

country_Germany
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
579 
True
 
28
ValueCountFrequency (%)
False579
95.4%
True28
 
4.6%
2025-11-08T15:51:28.553136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

country_Greece
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
596 
True
 
11
ValueCountFrequency (%)
False596
98.2%
True11
 
1.8%
2025-11-08T15:51:28.581611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

country_India
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
583 
True
 
24
ValueCountFrequency (%)
False583
96.0%
True24
 
4.0%
2025-11-08T15:51:28.612953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

country_Japan
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
601 
True
 
6
ValueCountFrequency (%)
False601
99.0%
True6
 
1.0%
2025-11-08T15:51:28.640858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

country_Netherlands
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
603 
True
 
4
ValueCountFrequency (%)
False603
99.3%
True4
 
0.7%
2025-11-08T15:51:28.668846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

country_Other
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
534 
True
73 
ValueCountFrequency (%)
False534
88.0%
True73
 
12.0%
2025-11-08T15:51:28.705766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

country_Spain
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
593 
True
 
14
ValueCountFrequency (%)
False593
97.7%
True14
 
2.3%
2025-11-08T15:51:28.734973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

country_United Kingdom
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
False
560 
True
 
47
ValueCountFrequency (%)
False560
92.3%
True47
 
7.7%
2025-11-08T15:51:28.763478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

country_United States
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size739.0 B
True
355 
False
252 
ValueCountFrequency (%)
True355
58.5%
False252
41.5%
2025-11-08T15:51:28.797463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

exp_x_role
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5930807
Minimum0
Maximum16
Zeros53
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:28.835618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median6
Q39
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5693254
Coefficient of variation (CV)0.54137445
Kurtosis-0.6793716
Mean6.5930807
Median Absolute Deviation (MAD)2
Skewness-0.034415136
Sum4002
Variance12.740084
MonotonicityNot monotonic
2025-11-08T15:51:28.891699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6124
20.4%
12105
17.3%
4104
17.1%
899
16.3%
970
11.5%
053
8.7%
328
 
4.6%
222
 
3.6%
162
 
0.3%
ValueCountFrequency (%)
053
8.7%
222
 
3.6%
328
 
4.6%
4104
17.1%
6124
20.4%
899
16.3%
970
11.5%
12105
17.3%
162
 
0.3%
ValueCountFrequency (%)
162
 
0.3%
12105
17.3%
970
11.5%
899
16.3%
6124
20.4%
4104
17.1%
328
 
4.6%
222
 
3.6%
053
8.7%

exp_x_remote
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7347611
Minimum0
Maximum4
Zeros127
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:28.947311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2
Q33
95-th percentile3
Maximum4
Range4
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.2120793
Coefficient of variation (CV)0.69870098
Kurtosis-1.3011954
Mean1.7347611
Median Absolute Deviation (MAD)1
Skewness-0.12272704
Sum1053
Variance1.4691362
MonotonicityNot monotonic
2025-11-08T15:51:29.006359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3199
32.8%
0127
20.9%
2120
19.8%
191
15.0%
1.527
 
4.4%
0.525
 
4.1%
418
 
3.0%
ValueCountFrequency (%)
0127
20.9%
0.525
 
4.1%
191
15.0%
1.527
 
4.4%
2120
19.8%
3199
32.8%
418
 
3.0%
ValueCountFrequency (%)
418
 
3.0%
3199
32.8%
2120
19.8%
1.527
 
4.4%
191
15.0%
0.525
 
4.1%
0127
20.9%

size_x_gdp
Real number (ℝ)

High correlation 

Distinct127
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130725.43
Minimum1907.0425
Maximum404897.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-11-08T15:51:29.079634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1907.0425
5-th percentile7042.3449
Q182627.505
median155721.82
Q3155721.82
95-th percentile233582.73
Maximum404897.45
Range402990.4
Interquartile range (IQR)73094.318

Descriptive statistics

Standard deviation63951.324
Coefficient of variation (CV)0.48920338
Kurtosis-0.19705682
Mean130725.43
Median Absolute Deviation (MAD)44200.196
Skewness-0.20412764
Sum79350337
Variance4.0897718 × 109
MonotonicityNot monotonic
2025-11-08T15:51:29.631459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155721.8226192
31.6%
213922.205259
 
9.7%
233582.733929
 
4.8%
92125.9828228
 
4.6%
71307.4017321
 
3.5%
142614.803519
 
3.1%
193204.522318
 
3.0%
112513.601513
 
2.1%
140779.42729
 
1.5%
6718.8415319
 
1.5%
Other values (117)210
34.6%
ValueCountFrequency (%)
1907.0425161
 
0.2%
2019.6570631
 
0.2%
2061.3562211
 
0.2%
2239.6138443
0.5%
2307.6149431
 
0.2%
2910.6384471
 
0.2%
3076.6456271
 
0.2%
3814.0850331
 
0.2%
3835.1920561
 
0.2%
4479.2276874
0.7%
ValueCountFrequency (%)
404897.44631
 
0.2%
283183.5321
 
0.2%
280994.3211
 
0.2%
247439.31781
 
0.2%
240168.38651
 
0.2%
233582.733929
4.8%
213922.205259
9.7%
209183.96211
 
0.2%
194991.0411
 
0.2%
193204.522318
 
3.0%

Interactions

2025-11-08T15:51:23.379651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:08.365580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:09.541663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:10.645291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:11.797767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:12.855770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:15.092753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:16.114431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:17.125089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:18.106240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:19.064558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:20.384410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:21.346756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:22.345830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:23.449836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:08.479917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:09.626358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:10.726148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:11.874750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:12.932419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:15.164237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:16.184891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:17.199334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:18.176996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:19.138654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:20.450214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:21.417662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:22.416708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:23.520886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:08.557054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:09.697759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:10.801550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:11.947392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:13.006371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:15.234016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:16.252821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:17.267462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:18.265656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:19.205444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:20.513480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:21.484332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:22.487458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:23.606203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-08T15:51:22.270555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T15:51:23.303089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-08T15:51:29.728680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Cost of Living IndexCost of Living Plus Rent IndexCountryGDPLocal Purchasing Power IndexRent Indexadjusted_salarycompany_sizecompany_size_numcountry_Francecountry_Germanycountry_Greececountry_Indiacountry_Japancountry_Netherlandscountry_Othercountry_Spaincountry_United Kingdomcountry_United Statesemployment_typeexp_x_remoteexp_x_roleexperience_levelexperience_numericgdp_to_power_ratiojob_titlelog_salaryremote_Onsiteremote_Remoteremote_ratiorent_burden_ratiorole_categoryrole_numericsalary_in_usdsalary_ksize_x_gdpwork_year
Cost of Living Index1.0000.9740.9650.8730.9250.9520.4280.1610.1610.2560.3700.7690.9160.4490.0860.6180.7030.4970.9740.0000.2940.2090.2320.2320.8110.2850.6150.0920.2960.2530.8540.0530.0530.6150.6160.7130.196
Cost of Living Plus Rent Index0.9741.0000.9650.8900.9530.9920.4530.1640.1640.3330.4730.5060.8990.3640.1390.7060.5750.6310.9940.0430.3030.2250.2380.2380.8060.2170.6360.1000.3290.2810.9150.0740.0740.6360.6360.7220.203
Country0.9650.9651.0000.8270.9660.9640.0000.3070.3070.9600.9600.9600.9600.9600.9600.9600.9600.9600.9600.2350.2700.1150.2810.2810.7920.2030.4470.1740.3660.3490.9650.0470.0470.0000.0000.5870.268
GDP0.8730.8900.8271.0000.8800.8880.4400.2040.2040.3060.4740.6180.6900.3730.1370.5420.5070.5740.9300.0000.3370.3100.2590.2590.9570.1410.6020.1330.3120.2750.8340.0420.0420.6020.6020.6830.409
Local Purchasing Power Index0.9250.9530.9660.8801.0000.9480.4440.1470.1470.4950.4600.7460.6990.1780.1270.6500.5230.6150.9930.0610.2710.2160.2200.2200.7580.1750.6120.1230.3250.2840.8960.0840.0840.6120.6120.7030.208
Rent Index0.9520.9920.9640.8880.9481.0000.4480.1830.1830.7300.6820.6210.8020.4510.1540.5060.4700.6600.9850.0380.3060.2300.2310.2310.7930.3130.6260.0770.2830.2820.9350.0870.0870.6260.6260.7120.244
adjusted_salary0.4280.4530.0000.4400.4440.4481.0000.1970.1970.1150.0000.0200.1250.1740.0000.2680.0000.0570.4370.1650.3570.2550.3500.3500.3930.3410.9500.0000.1540.1190.4320.1440.1440.9500.9500.4130.189
company_size0.1610.1640.3070.2040.1470.1830.1971.0001.0000.0000.0840.0530.0860.2450.1020.1990.0000.0420.2020.0280.2760.1900.2050.2050.3760.3040.2010.0700.1390.2030.1950.1390.1390.1750.1750.7020.420
company_size_num0.1610.1640.3070.2040.1470.1830.1971.0001.0000.0000.0840.0530.0860.2450.1020.1990.0000.0420.2020.0280.2760.1900.2050.2050.3760.3040.2010.0700.1390.2030.1950.1390.1390.1750.1750.7020.420
country_France0.2560.3330.9600.3060.4950.7300.1150.0000.0001.0000.0000.0000.0000.0000.0000.0130.0000.0000.1740.0000.1850.0880.0680.0680.3130.0000.1900.0000.1460.2410.6540.0570.0570.0810.1110.1010.127
country_Germany0.3700.4730.9600.4740.4600.6820.0000.0840.0840.0001.0000.0000.0000.0000.0000.0560.0000.0270.2500.0690.1410.0830.1480.1480.1990.2850.0260.0000.0720.1250.8320.1000.1000.0000.0000.1530.145
country_Greece0.7690.5060.9600.6180.7460.6210.0200.0530.0530.0000.0001.0000.0000.0000.0000.0000.0000.0000.1430.0000.1660.1600.1430.1430.2600.3780.1250.0000.0000.0000.4730.1250.1250.1090.1070.3080.060
country_India0.9160.8990.9600.6900.6990.8020.1250.0860.0860.0000.0000.0001.0000.0000.0000.0470.0000.0140.2290.0000.1170.1490.1350.1350.8390.2940.5060.0000.0470.0560.7170.0000.0000.2750.2740.5300.136
country_Japan0.4490.3640.9600.3730.1780.4510.1740.2450.2450.0000.0000.0000.0001.0000.0000.0000.0000.0000.0930.0000.1230.0440.0000.0000.1750.2680.0000.0000.0670.0880.4010.0000.0000.0510.0510.2440.092
country_Netherlands0.0860.1390.9600.1370.1270.1540.0000.1020.1020.0000.0000.0000.0000.0001.0000.0000.0000.0000.0640.1320.0560.0000.0000.0000.0500.0000.1340.0000.0000.0000.1470.0000.0000.0000.0000.2650.000
country_Other0.6180.7060.9600.5420.6500.5060.2680.1990.1990.0130.0560.0000.0470.0000.0001.0000.0000.0890.4320.0430.2190.1950.1970.1970.6160.3000.4210.0000.1550.1870.5180.1280.1280.3360.3370.4500.198
country_Spain0.7030.5750.9600.5070.5230.4700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.1660.0000.2180.0210.1350.1350.3050.0000.1570.0510.0460.0610.7330.0000.0000.1050.1030.2600.000
country_United Kingdom0.4970.6310.9600.5740.6150.6600.0570.0420.0420.0000.0270.0000.0140.0000.0000.0890.0001.0000.3350.0000.1670.1590.1640.1640.5950.0000.2100.1250.1480.1550.6550.1330.1330.1760.1750.4480.000
country_United States0.9740.9940.9600.9300.9930.9850.4370.2020.2020.1740.2500.1430.2290.0930.0640.4320.1660.3351.0000.0860.4190.3530.3700.3700.7730.2630.5960.0000.2930.3610.9810.1600.1600.5720.5760.7700.263
employment_type0.0000.0430.2350.0000.0610.0380.1650.0280.0280.0000.0690.0000.0000.0000.1320.0430.0000.0000.0861.0000.1460.1980.1220.1220.0690.3470.2060.0430.0640.1000.1530.0000.0000.1490.1490.0390.040
exp_x_remote0.2940.3030.2700.3370.2710.3060.3570.2760.2760.1850.1410.1660.1170.1230.0560.2190.2180.1670.4190.1461.0000.2780.7950.7950.3350.2210.3860.9960.8930.9050.3100.1100.1100.3860.3860.2070.306
exp_x_role0.2090.2250.1150.3100.2160.2300.2550.1900.1900.0880.0830.1600.1490.0440.0000.1950.0210.1590.3530.1980.2781.0000.6290.6290.3040.6240.2660.0000.1650.1490.2330.7610.7610.2660.2660.1790.229
experience_level0.2320.2380.2810.2590.2200.2310.3500.2050.2050.0680.1480.1430.1350.0000.0000.1970.1350.1640.3700.1220.7950.6291.0001.0000.2860.3660.3270.0800.1550.1380.2270.1700.1700.3580.3590.2640.230
experience_numeric0.2320.2380.2810.2590.2200.2310.3500.2050.2050.0680.1480.1430.1350.0000.0000.1970.1350.1640.3700.1220.7950.6291.0001.0000.2860.3660.3270.0800.1550.1380.2270.1700.1700.3580.3590.2640.230
gdp_to_power_ratio0.8110.8060.7920.9570.7580.7930.3930.3760.3760.3130.1990.2600.8390.1750.0500.6160.3050.5950.7730.0690.3350.3040.2860.2861.0000.2470.5520.0680.2430.2280.7360.0900.0900.5520.5520.6470.591
job_title0.2850.2170.2030.1410.1750.3130.3410.3040.3040.0000.2850.3780.2940.2680.0000.3000.0000.0000.2630.3470.2210.6240.3660.3660.2471.0000.2360.0000.1870.1800.2740.9620.9620.3260.3260.2350.260
log_salary0.6150.6360.4470.6020.6120.6260.9500.2010.2010.1900.0260.1250.5060.0000.1340.4210.1570.2100.5960.2060.3860.2660.3270.3270.5520.2361.0000.0000.1980.1700.5830.0990.0991.0001.0000.5570.231
remote_Onsite0.0920.1000.1740.1330.1230.0770.0000.0700.0700.0000.0000.0000.0000.0000.0000.0000.0510.1250.0000.0430.9960.0000.0800.0800.0680.0000.0001.0000.6630.9990.0290.0000.0000.0000.0000.1630.082
remote_Remote0.2960.3290.3660.3120.3250.2830.1540.1390.1390.1460.0720.0000.0470.0670.0000.1550.0460.1480.2930.0640.8930.1650.1550.1550.2430.1870.1980.6631.0000.9990.3510.1310.1310.2000.2050.1990.187
remote_ratio0.2530.2810.3490.2750.2840.2820.1190.2030.2030.2410.1250.0000.0560.0880.0000.1870.0610.1550.3610.1000.9050.1490.1380.1380.2280.1800.1700.9990.9991.0000.3110.1180.1180.1520.1560.1640.247
rent_burden_ratio0.8540.9150.9650.8340.8960.9350.4320.1950.1950.6540.8320.4730.7170.4010.1470.5180.7330.6550.9810.1530.3100.2330.2270.2270.7360.2740.5830.0290.3510.3111.0000.0930.0930.5830.5830.6500.239
role_category0.0530.0740.0470.0420.0840.0870.1440.1390.1390.0570.1000.1250.0000.0000.0000.1280.0000.1330.1600.0000.1100.7610.1700.1700.0900.9620.0990.0000.1310.1180.0931.0001.0000.0940.0920.0300.116
role_numeric0.0530.0740.0470.0420.0840.0870.1440.1390.1390.0570.1000.1250.0000.0000.0000.1280.0000.1330.1600.0000.1100.7610.1700.1700.0900.9620.0990.0000.1310.1180.0931.0001.0000.0940.0920.0300.116
salary_in_usd0.6150.6360.0000.6020.6120.6260.9500.1750.1750.0810.0000.1090.2750.0510.0000.3360.1050.1760.5720.1490.3860.2660.3580.3580.5520.3261.0000.0000.2000.1520.5830.0940.0941.0001.0000.5570.211
salary_k0.6160.6360.0000.6020.6120.6260.9500.1750.1750.1110.0000.1070.2740.0510.0000.3370.1030.1750.5760.1490.3860.2660.3590.3590.5520.3261.0000.0000.2050.1560.5830.0920.0921.0001.0000.5570.211
size_x_gdp0.7130.7220.5870.6830.7030.7120.4130.7020.7020.1010.1530.3080.5300.2440.2650.4500.2600.4480.7700.0390.2070.1790.2640.2640.6470.2350.5570.1630.1990.1640.6500.0300.0300.5570.5571.0000.432
work_year0.1960.2030.2680.4090.2080.2440.1890.4200.4200.1270.1450.0600.1360.0920.0000.1980.0000.0000.2630.0400.3060.2290.2300.2300.5910.2600.2310.0820.1870.2470.2390.1160.1160.2110.2110.4321.000

Missing values

2025-11-08T15:51:24.907504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-08T15:51:25.114554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Countrywork_yearexperience_levelemployment_typejob_titlesalary_in_usdremote_ratiocompany_sizeCost of Living IndexRent IndexLocal Purchasing Power IndexCost of Living Plus Rent IndexGDPlog_salaryexperience_numericsalary_kadjusted_salaryrent_burden_ratiogdp_to_power_ratiorole_categoryrole_numericremote_Onsiteremote_Remotecompany_size_numcountry_Francecountry_Germanycountry_Greececountry_Indiacountry_Japancountry_Netherlandscountry_Othercountry_Spaincountry_United Kingdomcountry_United Statesexp_x_roleexp_x_remotesize_x_gdp
0Germany2020MidFTData Scientist798330L62.20000024.400000120.20000044.00000047379.76519511.287705279.81283.490.555394.174AI4TrueFalse3FalseTrueFalseFalseFalseFalseFalseFalseFalseFalse80.0142139.295584
1Japan2020SeniorFTMachine Learning Scientist2600000S46.10000013.400000117.00000030.40000040028.73417312.4684413260.05639.910.441342.126AI4TrueFalse1FalseFalseFalseFalseTrueFalseFalseFalseFalseFalse120.040028.734173
2United Kingdom2020SeniorFTBig Data Engineer10902450M62.00000031.100000115.20000047.10000040404.80622411.5993323109.01758.450.660350.736Engineering2FalseFalse2FalseFalseFalseFalseFalseFalseFalseFalseTrueFalse61.580809.612448
3Honduras2020MidFTProduct Data Analyst200000S63.60621834.282185124.58890849.5072272307.6149439.903538220.0314.430.69218.522Analytics3TrueFalse1FalseFalseFalseFalseFalseFalseTrueFalseFalseFalse60.02307.614943
4United States2020SeniorFTMachine Learning Engineer15000050L70.40000041.700000142.30000056.60000064401.50743511.9183973150.02130.680.737452.576AI4FalseFalse3FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue121.5193204.522306
5United States2020EntryFTData Analyst72000100L70.40000041.700000142.30000056.60000064401.50743511.184435172.01022.730.737452.576Analytics3FalseTrue3FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue31.0193204.522306
6United States2020SeniorFTLead Data Scientist190000100S70.40000041.700000142.30000056.60000064401.50743512.1547853190.02698.860.737452.576AI4FalseTrue1FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue123.064401.507435
7Hungary2020MidFTData Scientist3573550L41.70000011.30000063.40000027.10000016386.93088210.483914235.7856.950.417258.469AI4FalseFalse3FalseFalseFalseFalseFalseFalseTrueFalseFalseFalse81.049160.792646
8United States2020MidFTBusiness Data Analyst135000100L70.40000041.700000142.30000056.60000064401.50743511.8130372135.01917.610.737452.576Analytics3FalseTrue3FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue62.0193204.522306
9New Zealand2020SeniorFTLead Data Engineer12500050S64.60000025.900000121.00000046.00000041850.92032811.7360773125.01934.980.563345.875Engineering2FalseFalse1FalseFalseFalseFalseFalseFalseTrueFalseFalseFalse61.541850.920328
Countrywork_yearexperience_levelemployment_typejob_titlesalary_in_usdremote_ratiocompany_sizeCost of Living IndexRent IndexLocal Purchasing Power IndexCost of Living Plus Rent IndexGDPlog_salaryexperience_numericsalary_kadjusted_salaryrent_burden_ratiogdp_to_power_ratiorole_categoryrole_numericremote_Onsiteremote_Remotecompany_size_numcountry_Francecountry_Germanycountry_Greececountry_Indiacountry_Japancountry_Netherlandscountry_Othercountry_Spaincountry_United Kingdomcountry_United Statesexp_x_roleexp_x_remotesize_x_gdp
597United States2022SeniorFTData Analyst170000100M70.441.7142.356.677860.91129112.0435603170.02414.770.737547.160Analytics3FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue93.0155721.822582
598United States2022MidFTData Scientist160000100M70.441.7142.356.677860.91129111.9829352160.02272.730.737547.160AI4FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue82.0155721.822582
599United States2022MidFTData Scientist130000100M70.441.7142.356.677860.91129111.7752972130.01846.590.737547.160AI4FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue82.0155721.822582
600Canada2022EntryFTData Analyst670000M64.833.2103.749.656256.80072611.112463167.01033.950.669542.496Analytics3TrueFalse2FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse30.0112513.601453
601Canada2022EntryFTData Analyst520000M64.833.2103.749.656256.80072610.859018152.0802.470.669542.496Analytics3TrueFalse2FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse30.0112513.601453
602United States2022SeniorFTData Engineer154000100M70.441.7142.356.677860.91129111.9447143154.02187.500.737547.160Engineering2FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue63.0155721.822582
603United States2022SeniorFTData Engineer126000100M70.441.7142.356.677860.91129111.7440453126.01789.770.737547.160Engineering2FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue63.0155721.822582
604United States2022SeniorFTData Analyst1290000M70.441.7142.356.677860.91129111.7675753129.01832.390.737547.160Analytics3TrueFalse2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue90.0155721.822582
605United States2022SeniorFTData Analyst150000100M70.441.7142.356.677860.91129111.9183973150.02130.680.737547.160Analytics3FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue93.0155721.822582
606United States2022MidFTAI Scientist200000100L70.441.7142.356.677860.91129112.2060782200.02840.910.737547.160AI4FalseTrue3FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue82.0233582.733873

Duplicate rows

Most frequently occurring

Countrywork_yearexperience_levelemployment_typejob_titlesalary_in_usdremote_ratiocompany_sizeCost of Living IndexRent IndexLocal Purchasing Power IndexCost of Living Plus Rent IndexGDPlog_salaryexperience_numericsalary_kadjusted_salaryrent_burden_ratiogdp_to_power_ratiorole_categoryrole_numericremote_Onsiteremote_Remotecompany_size_numcountry_Francecountry_Germanycountry_Greececountry_Indiacountry_Japancountry_Netherlandscountry_Othercountry_Spaincountry_United Kingdomcountry_United Statesexp_x_roleexp_x_remotesize_x_gdp# duplicates
23United States2022SeniorFTData Scientist140000100M70.441.7142.356.677860.91129111.8494053140.01988.640.737547.160AI4FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue123.0155721.8225826
7United States2022SeniorFTData Analyst90320100M70.441.7142.356.677860.91129111.411125390.31282.950.737547.160Analytics3FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue93.0155721.8225825
10United States2022SeniorFTData Analyst112900100M70.441.7142.356.677860.91129111.6342673112.91603.690.737547.160Analytics3FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue93.0155721.8225824
25United States2022SeniorFTData Scientist210000100M70.441.7142.356.677860.91129112.2548683210.02982.950.737547.160AI4FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue123.0155721.8225824
12United States2022SeniorFTData Analyst170000100M70.441.7142.356.677860.91129112.0435603170.02414.770.737547.160Analytics3FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue93.0155721.8225823
22United States2022SeniorFTData Scientist123000100M70.441.7142.356.677860.91129111.7199483123.01747.160.737547.160AI4FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue123.0155721.8225823
0Canada2022SeniorFTData Analyst61300100M64.833.2103.749.656256.80072611.023551361.3945.990.669542.496Analytics3FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse93.0112513.6014532
1Canada2022SeniorFTData Analyst130000100M64.833.2103.749.656256.80072611.7752973130.02006.170.669542.496Analytics3FalseTrue2FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse93.0112513.6014532
2Germany2021MidFTData Scientist9073450L62.224.4120.244.052265.65416211.415698290.71458.750.555434.822AI4FalseFalse3FalseTrueFalseFalseFalseFalseFalseFalseFalseFalse81.0156796.9624862
3Greece2022MidFTETL Developer549570M52.012.953.033.220971.83492610.914325255.01056.870.389395.695Engineering2TrueFalse2FalseFalseTrueFalseFalseFalseFalseFalseFalseFalse40.041943.6698522